The Child is Father of the Man: Foresee the Success at the Early Stage

TL;DR

This paper introduces a joint predictive model that forecasts the long-term scientific impact of research early on, addressing key challenges like feature design, non-linearity, and domain heterogeneity with scalable algorithms.

Contribution

It presents a novel, scalable optimization-based model that effectively predicts long-term scientific impact at early stages, overcoming several open challenges in the field.

Findings

The model outperforms existing methods in accuracy on large scholarly datasets.

The proposed algorithms are efficient and scalable for real-world applications.

Empirical results validate the effectiveness of early impact prediction.

Abstract

Understanding the dynamic mechanisms that drive the high-impact scientific work (e.g., research papers, patents) is a long-debated research topic and has many important implications, ranging from personal career development and recruitment search, to the jurisdiction of research resources. Recent advances in characterizing and modeling scientific success have made it possible to forecast the long-term impact of scientific work, where data mining techniques, supervised learning in particular, play an essential role. Despite much progress, several key algorithmic challenges in relation to predicting long-term scientific impact have largely remained open. In this paper, we propose a joint predictive model to forecast the long-term scientific impact at the early stage, which simultaneously addresses a number of these open challenges, including the scholarly feature design, the non-linearity, the domain-heterogeneity and dynamics. In particular, we formulate it as a regularized optimization problem and propose effective and scalable algorithms to solve it. We perform extensive empirical evaluations on large, real scholarly data sets to validate the effectiveness and the efficiency of our method.